Can InternLM 7B run on RX 9060 XT 16GB?

YES — Tight Fit

A72Great

Estimated from fit model

InternLM 7B needs ~14.6 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.6 GB, 47.2 tok/s, Tight fit

14.6 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

47.2 tok/s

TTFT

4101 ms

Safe context

Memory

14.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsInternLM 7B on RX 9060 XT 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 47.2 tok/s decode · 4.1s TTFT (warm) · 118 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	47.2 tok/s	2237 ms	8K
Coding	A	Tight fit	47.2 tok/s	4101 ms	8K
Agentic Coding	F	Too heavy	17.4 tok/s	16140 ms	8K
Reasoning	A	Tight fit	47.2 tok/s	4846 ms	8K
RAG	F	Too heavy	17.4 tok/s	20175 ms	8K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B68
NVFP4	4

Get started

Copy-paste commands to run InternLM 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "InternLM/InternLM-7B" \
  --hf-file "InternLM-7B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your RX 9060 XT 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	39.5 tok/s
Qwen 3 14B	14B	S	25.5 tok/s

Frequently asked questions

Can RX 9060 XT 16GB run InternLM 7B?

Yes, RX 9060 XT 16GB can run InternLM 7B with a A grade (Tight fit). Expected decode speed: 47.2 tok/s.

How much VRAM does InternLM 7B need?

InternLM 7B (7B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.

What is the best quantization for InternLM 7B?

The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will InternLM 7B run at on RX 9060 XT 16GB?

On RX 9060 XT 16GB, InternLM 7B achieves approximately 47.2 tokens per second decode speed with a time-to-first-token of 4101ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run InternLM 7B for coding?

For coding workloads, InternLM 7B on RX 9060 XT 16GB receives a A grade with 47.2 tok/s and 8K context.

What context window can InternLM 7B use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, InternLM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RX 9060 XT 16GB See all hardware for InternLM 7B

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<iframe src="https://willitrunai.com/embed/internlm-7b-on-rx-9060-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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